A graph-conditioned meta-optimizer learns QAOA parameter trajectories from one problem class and transfers them to others, yielding better initializations than standard methods in an empirical study of 64 settings.
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Hybrid quantum-classical optimization for unit commitment uses Pauli-Correlation Encoding to solve multi-period schedules with up to 312 binary variables while satisfying load, ramping, and reserve constraints.
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Graph-Conditioned Meta-Optimizer for QAOA Parameter Generation on Multiple Problem Classes
A graph-conditioned meta-optimizer learns QAOA parameter trajectories from one problem class and transfers them to others, yielding better initializations than standard methods in an empirical study of 64 settings.
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Scaling Quantum Optimization for Unit Commitment via Pauli Correlation Encoding
Hybrid quantum-classical optimization for unit commitment uses Pauli-Correlation Encoding to solve multi-period schedules with up to 312 binary variables while satisfying load, ramping, and reserve constraints.